Devices And Methods For Combined Optical And Magnetic Resonance Imaging

ABSTRACT

Optical devices for use with a magnetic resonance imaging breast compression system include light wands and optical adapters that can releasably mate with grids. These devices, and their associated methods, may reduce or eliminate the need for biopsy by allowing for the differentiation of cancerous tumors, non-cancerous tumors, calcifications and cysts.

RELATED APPLICATIONS

This application claims the benefit of priority to commonly-owned andcopending U.S. Provisional Patent Application No. 60/834,374, filed 31Jul. 2006, which is incorporated herein by reference.

U.S. GOVERNMENT RIGHTS

This invention was made with Government support under grant nos.RO1CA69544, RO1CA109558, PO1CA80139 and U54CA105480, awarded by theNational Institutes of Health, and grant no. DAMD17-03-1-0405, awardedby the Department of Defense. The Government has certain rights in thisinvention.

BACKGROUND

X-ray mammography is generally regarded as the single most importanttool in the early detection of breast cancer. Mammography detects 85-90%of breast cancers, and the American Cancer Society recommends that womenage forty and older undergo yearly screening. The main drawback ofmammography is that it has a low positive predictive value whichfrequently necessitates additional testing, such as more intensivemammography, magnetic resonance (MR) imaging, ultrasound and/or biopsy.

MR imaging produces higher resolution images of deeper and/or densertissue than mammography, without the use of ionizing radiation. However,MR instruments are expensive to own and operate, and the resultingimages suffer from the same low positive predictive value as mammograms.Masses detected by MR imaging require further evaluation, such as byultrasound, which can distinguish between solid tumors and cysts, and/orbiopsy, which is an invasive and unpleasant procedure.

As an alternative to biopsy, optical imaging of tissue is an emergingmodality for detection, diagnosis and monitoring of breast cancer.Diffuse optical tomography uses electromagnetic energy, ranging fromvisible light to near infrared (NIR), to probe objects beneath the skinsurface, such as tissue, fluid and tumors. Information about tissuecomposition and morphology is gained by measuring and modeling lightabsorption, scattering and emission. This information can be used tocreate two-dimensional cross-sectional slices and/or three-dimensionalimages, and also to distinguish between cancerous tumors, non-canceroustumors, calcifications and cysts. For example, when light is absorbed bya compound (chromophore) within the tissue, the chromophore, such ashemoglobin, lipid or water, can be identified and quantified. Tumorsfrequently have increased blood flow, so that a high concentration ofhemoglobin may be indicative of a tumor. Further, intense scattering oflight may be attributed to a solid or semi-solid mass, such as a tumor.

Qualitative, moderate-resolution optical images have been used todiagnose tumors based on their metabolic and functional status, butimprovements in quantitative accuracy and resolution may be obtainedwhen anatomical information from other modalities, such as MR imaging orultrasound, is used in the image reconstruction procedure. Hybridinstrumentation and methodologies for incorporating anatomicalinformation as spatial priors into tissue reconstruction algorithms arecurrently being developed, and can significantly improve the accuracy ofthe recovered information by identifying borders between differenttissue types, as observed by MR.

One hybrid system for combined optical and MR imaging is described in V.Ntziachristos, X. H. Ma, and B. Chance, “Time-correlated single photoncounting imager for simultaneous magnetic resonance and near-infraredmammography”, Rev. Sci. Instrum., 69(12), 4221-4233, December 1998. Thissystem utilizes a pair of soft compression plates that contain MR radiofrequency coils, as well as optical fibers. The medial compression platecontains optical source fibers and the lateral compression platecontains optical detector fibers, so that light transmission may bedetected.

Such specialized systems are infrequently adopted as replacements forcurrent industry standards because they are viewed as time consuming andtroublesome. For example, the orientation and field gradient of theabove-described integrated radio frequency coils must be recalibratedfor each patient, and the system does not allow for biopsy access. Aphysician must therefore choose, before placing the patient into thebore of the MR instrument, whether a mass (if observed) will beoptically imaged, using the specialized compression system, or biopsied,using a traditional compression system. If the physician chooses opticalimaging and later, based on the optical imaging results, decides tobiopsy the mass, the patient must be removed from the MR instrument,outfitted with a different compression system, placed back in theinstrument and a new series of scans must be taken to relocate the mass.This extra procedure is an expensive and arduous task ties up valuablemedical resources.

SUMMARY

In one embodiment, an optical adapter for combined optical imaging andmagnetic resonance imaging of breast tissue includes a housing having anoptical window, wherein a portion of the housing containing the opticalwindow releasably mates with a grid hole of a breast tissue compressionsystem.

In one embodiment, a method of optically imaging breast tissue includescoupling an optical adapter with a grid of a magnetic resonance imagingbreast tissue compression system and obtaining optical data.

In one embodiment, a method uses spatial priors to increase theresolution and accuracy of a near infrared image reconstruction oftissue. The image reconstruction, which involves the use of aregularization reconstruction algorithm, is improved by incorporating afilter matrix into the regularization reconstruction algorithm, whereinthe filter matrix is generated by assigning each node in a finiteelement method mesh to a tissue type.

In one embodiment, a software product includes instructions, stored oncomputer-readable media, wherein the instructions, when executed by acomputer, perform steps for creating a tomographic image of tissue. Theinstructions include: instructions for obtaining magnetic resonance (MR)data associated with the tissue; instructions for generating a finiteelement method mesh from the MR data; instructions for assigning eachnode in the finite element method mesh to a tissue type; instructionsfor using the tissue type to generate a filter matrix for use in aregularization reconstruction algorithm; instructions for obtaining nearinfrared data associated with the tissue; instructions for using themagnetic resonance data that has been generated by the regularizationreconstruction algorithm to spatially constrain the near infrared dataalgorithm; and instructions for creating a tomographic image of thetissue.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a side plan view of a patient positioned for magneticresonance imaging of breast tissue.

FIG. 2 illustrates a breast coil and compression system for use duringmagnetic resonance imaging.

FIG. 3 illustrates an inferior sagittal view of a right breastimmobilized in the compression system of FIG. 2.

FIG. 4 illustrates the system of FIG. 3 further including an opticaladapter, according to one embodiment.

FIG. 5 illustrates a perspective view of an optical adapter, accordingto one embodiment.

FIG. 6 illustrates a perspective view of another optical adapter,according to one embodiment.

FIG. 7 illustrates a side plan view of an optical adapter.

FIG. 8 shows the connectivity between a grid and an optical adapter.

FIG. 9 illustrates an optical adapter in the form of a light wand,according to one embodiment.

FIG. 10 illustrates one process for constraining a near infrared imagewith magnetic resonance data, according to one embodiment.

FIG. 11 illustrates a block diagram of a system for implementing theprocess shown in FIG. 10.

FIG. 12 illustrates chromophore and scatter parameters for a tissuephantom using various model constraints, according to multipleembodiments.

DETAILED DESCRIPTION

Optical devices for use with a MR imaging breast compression system aredisclosed herein. Use of these devices and related methods may, forexample, allow for non-invasive differentiation between canceroustumors, non-cancerous tumors, calcifications, cysts, fatty tissue andfibroglandular tissue.

Reference will now be made to the attached drawings, where like numbersrepresent similar elements in multiple figures. Numbering withoutparentheses is used to denote a genus (e.g., optical adapter 400),whereas numbering with parentheses denotes a species within a genus(e.g., optical adapter 400(2)). Multiple elements within a figure maynot be labeled for the sake of clarity.

FIG. 1 shows a side plan view of a patient 100 positioned for MR imagingof breast tissue. Patient 100 lies prone on an MR imaging table 102,which slides, in the direction of arrow A, into the bore of an MRinstrument 104. The patient's breast 106 is positioned in a breast coil108. Breast coil 108 is used to apply a radio frequency pulse thatexcites hydrogen atoms in the body (e.g., water) to a high-energy state.When the radio frequency pulse is turned off, the hydrogen atoms releaseenergy as they return to a lower energy state. Breast coil 108 detectsthe released energy, and transmits data to a computer 110. Amicroprocessor 112 executing software 114, within a memory 116 ofcomputer 110, may manipulate the data to create an image of breast 106.Memory 116 may, for example, represent one or more of random accessmemory (RAM), erasable programmable read-only memory (EPROM),programmable read only memory (PROM) and non-volatile random accessmemory (NVRAM) (e.g., flash memory), or other types of non-volatilestorage.

FIG. 2 illustrates a breast coil 108 and compression system 200 for useduring magnetic resonance imaging. Breast coil 108 includes a bodycradle 204 that supports the torso of patient 100. Body cradle 204 hasone or two openings 206 that receive the patient's breast(s) 106. Bodycradle 204 is separated from a base 208 by pedestals 210. Base 208 mayinclude one or more openings 212 in vertical alignment with openings206. Compression system 200 is used to immobilize breast tissue duringimaging and biopsying. In operation, compression system 200 is disposedbetween body cradle 204 and base 208 of breast coil 108. Compressionsystem 200 includes a grid 214, a compression plate 220 and a biopsyneedle guide 226. Compression plate 220 includes a wall 222 that abutsthe medial edge of breast 106. A pair of threaded guides 224 allowscompression plate 220 to be mated with grid 214 via guide holes 218.Grid 214 abuts the lateral edge of breast 106. Grid 214 may be held inplace by fasteners (not shown) that screw onto threaded guides 224. Grid214 includes a plurality of grid holes 216 that provide access to breast106 and directional guidance during biopsying. Biopsy needle guide 226may be inserted into one of grid holes 216 and a biopsy needle (notshown) may be inserted through opening 228. FIG. 3 shows an inferiorsagittal view of a right breast 106 immobilized in compression system200 of FIG. 2.

FIG. 4 illustrates the system of FIG. 3 further including an opticaladapter 400. Optical adapter 400 is a modular device that can mate withgrid 214. Optical adapter 400 delivers electromagnetic energy, λ,produced by an excitation unit 402 and transmitted through fiber optics404, to breast tissue 106. Excitation unit 402 may have one or more of awhite light, laser or light emitting diode source. Electromagneticenergy that is reflected and/or emitted by breast tissue 106 istransmitted through optical adapter 400 to fiber optics 404. Thereflected and/or emitted light is transmitted to a detector 406.Depending upon the wavelength range of interest, detector 406 may, forexample, be a photodiode, spectrometer, CCD and/or a microbolometer.Detector 406 transmits raw data to computer 110, where the data may bestored to memory 116 and manipulated by microprocessor 112 executingsoftware 414. Tomographic images may be generated from the manipulateddata as concentration maps displaying a chromophore concentration, afluorophore concentration, scatter amplitude, and/or scatter power as aspatial function.

In one embodiment, wall 222 may be replaced by a grid 214 and,optionally, an optical adapter 400. Replacement of wall 222 may, forexample, be useful when optical imaging and/or biopsy are to beperformed on a suspected tumor that is proximal to the medial edge ofbreast 106, or when electromagnetic energy produced at the lateral edgeof breast 106 cannot penetrate the entire tissue volume. Electromagneticenergy in the near infrared (NIR) region is known to penetrate breasttissue to a depth of about 15 cm; however, some women have breast tissuethat exceeds this range and others, especially young women, have densebreast tissue which may limit light penetration to less than 15 cm. Inanother embodiment, use of multiple grids and optical adapters mayprovide more comprehensive and/or more accurate results than dataobtained from a single optical adapter 400. For example, a configurationcontaining two grids, each coupled to an optical adapter, may allow formeasurement of transmitted light—in addition to reflected and emittedlight.

FIG. 5 illustrates a perspective view of an optical adapter 400(1).Optical adapter 400(1) includes a housing 502 having protrusions 500that fit into grid holes 216. Protrusions 500 include optical windows504 that are transparent to a specified range of electromagnetic energy,e.g., visible and/or near infrared energy (λ=650-900 nm). Suitabletransparent materials for the visible and near infrared spectral rangeinclude quartz, polystyrene, polycarbonate and polypropylene. FIG. 6illustrates a perspective view of another optical adapter 400(2), whichis formed as a 5×5 matrix of protrusions 500. Each protrusion 500includes one or more source fibers 506 and one or more detector fibers508. In the example shown, six source fibers 506 surround one detectorfiber 508. It will be appreciated, however, that the number andconfiguration of source and detector fibers may vary. A side plan viewof optical adapter 400(2) is shown in FIG. 7. The connectivity betweengrid 214 and optical adapter 400 is shown in FIG. 8. A fiber opticconnection port 802 disposed on the posterior portion of optical adapter400 is also visible in FIG. 8.

Clamps, brackets, adhesives or other means of securing optical adapter400 to grid 214 may be integrated with or utilized in conjunction withoptical adapter 400 and/or grid 214.

FIG. 9 illustrates an optical adapter in the form of a light wand 900having a housing 502 and a single protrusion 500. For ease offabrication, it will be appreciated that optical window 504 may befixedly attached to protrusion 500, which may subsequently be connectedto housing 502. In an alternate embodiment, protrusion 500 may beexcluded and optical window 504 may be joined directly with housing 502.Light wand 900 may be used to investigate a suspicious area, which hasbeen detected by MR imaging. Light wand 900 may be oriented in grid 214according to protocols currently used for orienting biopsy needle guide226. In an alternate embodiment, light wand 900 is sized and shaped tofit in opening 228 of biopsy needle guide 226.

The optical devices and associated components described herein may belocated in or near the magnetic field of an MR instrument. They shouldtherefore be fabricated from non-magnetic materials such as plastic,glass, rubber, carbon fiber, non-magnetic metals (e.g., Ti, Zr, Zn, Sn,Cu, Ag, Au) and combinations thereof. Any magnetic materials that arenecessary within the optical devices and associated components areshielded.

Imaging agents may be used to improve the specificity and sensitivity ofmeasurements. For example, to improve the quality of MR images, magneticparticles, such as gadolinium, may be injected into a patient'scirculatory system directly upstream from a tissue to be imaged. Indiffuse optical tomography, intravenous administration of iodocyaninegreen is common. For both types of imaging, molecular specific contrastagents are currently being developed. Molecular specific contrast agentsselectively target tumors expressing certain proteins. They may,therefore, be used to identify a tumor and gain knowledge of itsimmunohistochemistry, which may help physicians prescribe targetedpharmaceuticals.

Image accuracy and quality may also be improved by the use of priorinformation in an image reconstruction methodology. An exampleillustrating the use of prior information in image reconstruction isprovided below. This example is for purposes of illustration only andnothing therein should be construed as limiting the scope of what isdescribed and claimed.

EXAMPLE 1 Use of a Laplacin-Type Regularization to Incorporate MagneticResonance Structure into NIR Images

Early work on incorporating prior knowledge of tissue structure from MRdata into NIR reconstructions imposed constraints on neighboring pixelssuch that within homogeneous regions the pixels had similar intensitylevels, and, in regions that exhibited distinctly different tissuecharacteristics, smoothing across the shared boundary was limited. Itwas further assumed that optical contrast correlated to MR contrast.While generally effective in simulation studies, and for reconstructingsimple phantom geometries containing a single discrete heterogeneity(i.e., inclusion), these methods proved vulnerable to over-biasing theinverse solutions toward the assumed distributions. Sensitivity to noisein the data and error in the region designation caused the algorithms tobe unreliable when imaging complex and layered phantoms.

The present method guides the iterative evolution of reconstruction, butdoes not impose a rigid constraint of interregion homogeneity. Portionsof the present method are described in B. Brooksby, S. Jiang, H.Dehghani, B. W. Pogue, K. D. Paulsen, J. Weaver, C. Kogel, S. P. Poplack“Combining near-infrared tomography and magnetic resonance imaging tostudy in vivo breast tissue: implementation of a Laplacian-typeregularization to incorporate magnetic resonance structure”, J. Biomed.Optics 10(5), 051504, September/October 2005 and B. Brooksby, S.Srinivasan, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J.Weaver, C. Kogel, S. P. Poplack “Spectral priors improve near-infrareddiffuse tomography more than spatial priors” Optics Letters, 30(15),1968-1970, Aug. 1, 2005, both of which are incorporated herein byreference.

FIG. 10 illustrates one process 1000 for constraining a near infraredimage with MR data. Process 1000 begins with the acquisition of MR datain step 1002. In step 1004, a two-dimensional or three-dimensionalfinite-element method mesh may be generated from the MR data, and NIRsource fiber locations may be located, e.g., by the presence offiduciaries, and correlated with the MR mesh. In step 1006, tissue types(e.g., adipose or fibroglandular) may be assigned to each node in themesh, and in step 1008, these tissue types may be used to generate afilter matrix for incorporation into a regularization reconstructionalgorithm that is used in step 1010 to reconstruct the MR image. In step1012, NIR data may be acquired, although it will be appreciated by thoseskilled in the art that MR and NIR data may be acquired simultaneouslyin some embodiments of process 1000. In step 1014, the MR structuralinformation may be used to constrain the NIR reconstruction. In step1016, absorption and scattering may be reconstructed at one or morewavelengths. In step 1018, physiological parameters, such as [Hb_(T)],S_(t)O₂, H₂O, A and b, may be computed.

FIG. 11 shows a block diagram 1100 of a system for implementing theprocess shown in FIG. 10. System 1100 includes tissue 106, opticaladapter 400, excitation unit 402, detector 406, breast coil 108,computer 110 and a display 1102. A tissue model, or phantom, may be usedin place of tissue 106 during development and calibration. Excitationunit 402 and breast coil 108 are controlled by computer 110 executingsoftware 414 that performs the steps of process 1000. NIR and MR signalsare measured by detector 406 and breast coil 108, respectively. Themeasured data are acquired (FIG. 10, steps 1002, 1012) by computer 110,and may, for example, be stored in memory 116. Process 1000 is then usedto manipulate the data and the resulting absorption, scattering andphysiological parameters (FIG. 10, steps 1016, 1018) are used togenerate a tomographic image using algorithms known in the art, whichare executed according to the steps of process 1104. The tomographicimage may be viewed on display 1102.

Image Formation

It is well established that in the interaction of NIR light with tissue,scattering dominates over absorption. Under these conditions, lighttransport can be effectively modeled using the diffusion equation overmoderately large distances. A frequency-domain diffusion model is usedto simulate measured signals for any specified distribution ofabsorption and reduced scattering coefficients, μ_(a) and μ′_(s), withinan imaged volume. This is given by: $\begin{matrix}{{{{{{- \nabla} \cdot {D(r)}}{\nabla{\Phi\left( {r,\omega} \right)}}} + {\left\lbrack {{\mu_{a}(r)} + \frac{\mathbb{i}\omega}{c}} \right\rbrack{\Phi\left( {r,\omega} \right)}}} = {S\left( {r,\omega} \right)}},} & (1)\end{matrix}$where S(r, ω) is an isotropic light source at position r, Φ(r, ω) is thephoton density at r, c is the speed of light in tissue, ω is thefrequency of light modulation, and D=1/[3(μ_(a)+μ′_(s))] is thediffusion coefficient. The reduced scattering coefficient is given byμ′_(s)=μ_(s) (1−g), where g is the mean cosine of the single scatterfunction (the anisotropy factor), and μ_(s) is the scatteringcoefficient. A type III boundary condition is applied as:$\begin{matrix}{{{\Phi + {\frac{D}{\alpha}{\hat{n} \cdot {\nabla\quad\Phi}}}} = 0},} & (2)\end{matrix}$where α is a term that incorporates reflection as a result of refractiveindex mismatch at the boundary, and {circumflex over (n)} is theoutward-pointing normal to the boundary.

Eq. (1) can be viewed as a nonlinear function of the optical properties.Its solution is represented as a complex-valued vector, y=F(μ_(a), D) (Frepresenting the model), having real and imaginary components that aretransformed to logarithm of the amplitude and phase in the measurements.The phase shift of the signal provides data that is dominated by theoptical path length through tissue, while the amplitude of thetransmitted light provides information about the overall attenuation ofthe signal. These measurements constitute the dataset necessary forsuccessful estimation of both absorption and reduced scatteringcoefficients.

Data acquired by the detection system is typically processed with afinite element method (FEM)-based reconstruction algorithm to generatetomographic images of μ_(a) and μ′_(s). In the image reconstruction, aNewton-minimization approach is used to seek a solution to:({circumflex over (μ)}_(a) , {circumflex over (D)})=min_(μ) _(a,D){∥y*−F(μ_(a) , D)∥+λ∥({circumflex over (μ)}_(a) , {circumflex over(D)})−(μ_(a,0) , D ₀)∥},   (3)where ∥·∥ represents the square root of the sum of squared elements, andλ is a weighting factor of the difference between the current values ofthe optical properties and their initial estimates and data-model misfit(y*−F(μ_(a), D), where y* is the experimental data). The magnitude ofthis objective function is sometimes referred to as the projection errorand provides a value for determining the convergence of the iterativesolution. Its minimum is evaluated by setting first derivatives withrespect to μ_(a) and D equal to zero. This leads to a set of equationsthat can be solved iteratively, using the following matrix equationderived from Eq. (3):δμ=(J ^(T) J+λI)⁻¹ └J ^(T) [y*−F(μ_(a) , D)]−λ(μ−μ₀)┘.   (4)where μ denotes the optical parameters being reconstructed and μ₀ is theoriginal estimate. At each iteration, the new set of μ_(a) and D valuesis updated by μ_(a) ^(i+1)=μ_(a) ^(i)+δμ_(a) ^(i), andD^(i+1)=D^(i)δD^(i), where i is the index for the iteration number, J isthe Jacobian matrix for the diffusion equation solution, and J^(T)J isill-conditioned and therefore regularized through the addition of λI,where I is an identity matrix. Regularization is implemented in aLevenberg-Marquardt algorithm where λ starts at a high value (typicallyten times the maximum value of the diagonal of J^(T)J) and can then besystematically reduced at each iteration. In Eq. (4), μ₀ is the initialestimate of optical properties input into the iterative estimationprocess, and is a form of prior information. Here, the initial estimateis determined through a data calibration procedure which assumes ahomogeneous property distribution.Inclusion of Priors

A priori information can be incorporated directly through the objectivefunction by formulating the minimization of a two term functional:({circumflex over (μ)}_(a) , {circumflex over (D)})=min_(μ) _(a,D){∥y*−F(μ_(a) , D)∥+α∥[L({circumflex over (μ)}_(a) , {circumflex over(D)})−(μ_(a,0) , D ₀)]∥}.   (5)The constant α balances the effect of the prior with the data-modelmismatch. The filter matrix L is generated using MR-derived priors andeffectively relaxes the smoothness constraints at the interface betweendifferent tissues, in directions normal to their common boundary. Theeffect on image quality is similar to that achieved through totalvariation minimization schemes. This procedure, however, is more robustand can easily encode internal boundary information from MR images. Eachnode in the FEM mesh is labeled according to the region, or tissue type,with which it is associated (in the MR image, e.g., adipose orfibroglandular). For the i'th node of n in region N, L_(i,1)=1. Whennodes i and j are in the same region, L_(i,j)=−1/n, otherwise L_(i,j)=0.The solution to Eq. (5) is accomplished with a Newton-minimizationapproach, that produces the update equation:δμ=(J ^(T) J+αL ^(T) L)⁻¹ └J ^(T) [y*−F(μ_(a) , D)]−αL ^(T)L[({circumflex over (μ)}_(a) , {circumflex over (D)})−(μ_(a,0) , D ₀)]┘,  (6)which can also be iteratively solved. Note L^(T)L approximates asecond-order Laplacian smoothing operator within each region separately.This construction of L has proved flexible and effective, but otherforms can easily be implemented and evaluated.

Simulation studies were performed to characterize the effect of L and αon the quality and quantitative accuracy of reconstructed images, and toestablish a value of α that can be used routinely. Data was generatedfrom numerical phantoms with a variety of heterogeneity patterns—rangingfrom a simple circular anomaly in a homogeneous background to irregulardistributions of regions with two or three different properties. Noise(1% to 2%) was added to simulated data to better replicate experimentalconditions. Error was also added to the a priori region designation, toaccount for the small loss of resolution when spatial information istransferred from MR images to FEM meshes. Images were reconstructed fromthis data using a range of α from 1 to 100. A high α value increases theimpact of the spatial prior, leading to images with sharper internalboundaries, but could negatively bias solutions if this prior is notcorrect. By accounting for the different sources of error that can bepresent when data is acquired, simulation results indicate that settingα to ten times the maximum value of the diagonal of J^(T)J optimizesimage quality and accuracy regardless of the level of geometriccomplexity present in the area under investigation.

Spectral Decomposition

The absorption coefficient at any wavelength is assumed to be a linearcombination of the absorption due to all relevant chromophores in thesample: $\begin{matrix}{{{\mu_{a}(\lambda)} = {\sum\limits_{i = 1}^{N}{{ɛ\left( {i,\lambda} \right)}C_{i}}}},} & (7)\end{matrix}$where λ is the molar absorption spectra, and C is the concentration ofeach chromophore. The concentrations of three chromophores—oxyhemoglobin(HbO₂), deoxyhemoglobin (Hb) and water (H₂O)—are estimated. Hence, givenμ_(a) at the k'th pixel for multiple wavelengths, a linear inversion ofEq. (7) determines the array of C values:C _(k) =E ⁻¹μ_(a,k),   (8)representing the concentrations of the three chromophores. In Eq. (8), Eis the matrix of molar extinction coefficients having elements ε(i, λ),for the i'th chromophore at different wavelengths.

The spectral character of the reduced scattering coefficient alsoprovides information about the composition of the tissue. From anapproximation to Mie scattering theory, it is possible to derive arelation between μ′_(s) and wavelength given by:μ′_(s)(λ)=Aλ ^(−b),   (9)where b is the scattering power and A is the scattering amplitude (whichdepend on scatterer size and number density). Typically, largescatterers have lower b and A values. These scattering parameters appearto reflect variations in structural breast composition associated withage and radiographic density.

In one embodiment, MR data may provide absolute blood volume, waterand/or lipid concentrations. When the absolute blood volume, water andlipid concentrations are input into Eq. (7), unknown values are reducedso that the reconstruction may solve only for oxy or deoxy hemoglobin,scatter and exogenous contrast. Such a decrease in the number ofunknowns provides a potentially more accurate quantification, and mayreduce the time needed to perform the reconstruction.

Phantom Studies

A two-layer gelatin phantom with a cylindrical inclusion embedded insidethe inner layer was used to evaluate the ability of the NIR-MRalgorithm. Each gel layer possessed a different absorption and reducedscattering coefficient. When the MR data was neglected, and amplitudeand phase data were reconstructed with a standard Newton typereconstruction, the root mean square (rms) error of the recovereddistributions of the absorption and reduced scattering coefficients wereestimated to be 0.0023 and 0.230, respectively. When the full MR dataset was utilized, and MR-derived priors guided the reconstruction, therms error of the absorption and reduced scattering coefficient imagesdecreased 43% to 0.0014, and 55% to 0.104, respectively. The mean valueof the absorption coefficient estimated in the region of the inclusionwas accurate to within 10%, and estimation of the reduced scatteringcoefficient improved to within 20%.

A second phantom, having a homogeneous body with a 22 mm cylindricalcavity filled with a 3:1 absorption contrast intralipid solution, wasalso studied. When prior information was used in image reconstruction,the rms error of the absorption and reduced scattering coefficientimages decreased from 0.0019 to 0.0014 (26%) and from 0.1444 to 0.0613(58%), respectively.

FIG. 12 illustrates chromophore and scatter parameters for a tissuephantom using various model constraints. The top row shows trueproperties of the phantom including: total hemoglobin concentration(μM), [Hb_(T)]; oxygen saturation (%), S_(t)O₂; water (%), H₂O;scattering amplitude, A; and scattering power, b. The second row fromthe top illustrates a NIR reconstruction using no priors. It can be seenthat the conventional method (no priors) yields images with considerableartifacts. The middle row illustrates a reconstruction incorporatingspatial priors from MR data. Spatial priors remove the artifacts, sothat the inclusion is clearly visible and matches the expected size andshape. However, the [Hb_(T)] contrast is significantly underestimated;the recovered mean in the region of the anomaly reaches only 57% of thetrue value. The second row from the bottom illustrates a reconstructionincorporating spectral priors from MR data. The spectral priors showsubstantial improvement in the quantification with the mean [Hb_(T)] at78% of the true value. The bottom row incorporates both spatial andspectral priors. The application of both constraints results in imageswith a further reduction in artifacts close to the boundary, and themean [Hb_(T)] reaches 88% of the expected value.

The changes described above, and others, may be made in the devices andmethods described herein without departing from the scope hereof. Itshould thus be noted that the matter contained in the above descriptionor shown in the accompanying drawings should be interpreted asillustrative and not in a limiting sense. The following claims areintended to cover all generic and specific features described herein, aswell as all statements of the scope of the present method and device,which, as a matter of language, might be said to fall there between.

1. An optical adapter for combined optical imaging and magneticresonance imaging of breast tissue, comprising: a housing having anoptical window, wherein a portion of the housing containing the opticalwindow releasably mates with a grid hole of a breast tissue compressionsystem.
 2. The optical adapter of claim 1, wherein the optical window isfabricated from a material selected from quartz, polystyrene,polycarbonate and polypropylene.
 3. The optical adapter of claim 1,wherein the housing is fabricated from plastic, glass, rubber, carbonfiber, non-magnetic metals and combinations thereof.
 4. The opticaladapter of claim 1, wherein the housing comprises a plurality of opticalwindows forming a matrix.
 5. The optical adapter of claim 1, furthercomprising fiber optics for coupling electromagnetic energy between theoptical window and emission and detection devices.
 6. The opticaladapter of claim 1, wherein the optical adapter forms a light wand. 7.The optical adapter of claim 1, wherein the optical adapter is sized andshaped to fit within an opening of a biopsy needle guide.
 8. A method ofcombining magnetic resonance imaging and optical imaging of breasttissue, comprising: coupling an optical adapter with a grid of amagnetic resonance imaging breast tissue compression system; andobtaining optical data.
 9. The method of claim 8, further comprisingusing the optical data to create a tomographic image of the breasttissue.
 10. The method of claim 9, further comprising evaluating thetomographic image to make a diagnosis of damage or disease to thetissue.
 11. The method of claim 8, further comprising obtaining magneticresonance data that provide quantification of one or more of lipidconcentration, water concentration, hemoglobin concentration,deoxyhemoglobin concentration and blood volume.
 12. The method of claim11, further comprising using the optical data and the magnetic resonancedata to create a tomographic image of the breast tissue.
 13. The methodof claim 12, further comprising evaluating the tomographic image to makea diagnosis of damage or disease to the tissue.
 14. In a method of usingspatial priors to increase the resolution and accuracy of near infraredimage reconstruction of tissue, the image reconstruction involving theuse of a regularization reconstruction algorithm, an improvementcomprising: incorporating a filter matrix into the regularizationreconstruction algorithm, wherein the filter matrix is generated byassigning each node in a finite element method mesh to a tissue type.15. The method of claim 14, further comprising determining one or moreof chromophore concentration, scatter amplitude, scatter power andfluorophore concentration from near infrared data.
 16. The method ofclaim 15, further comprising determining a concentration of imagingagents from the fluorophore concentration.
 17. The method of claim 14,further comprising evaluating the reconstructed image to make adiagnosis of damage or disease to the tissue.
 18. A software productcomprising instructions, stored on computer-readable media, wherein theinstructions, when executed by a computer, perform steps for creating atomographic image of tissue, comprising: instructions for obtainingmagnetic resonance (MR) data associated with the tissue; instructionsfor generating a finite element method mesh from the MR data;instructions for assigning each node in the finite element method meshto a tissue type; instructions for using the tissue type to generate afilter matrix for use in a regularization reconstruction algorithm;instructions for obtaining near infrared data associated with thetissue; instructions for using the magnetic resonance data that has beengenerated by the regularization reconstruction algorithm to spatiallyconstrain the near infrared data algorithm; and instructions forcreating a tomographic image of the tissue.
 19. The software product ofclaim 18, further comprising instructions for obtaining one or more of ascatter amplitude, a scatter power, a chromophore concentration and afluorophore concentration from the near infrared data.